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Using activity sensitivity and network topology information to monitor project time performance

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  • Vanhoucke, Mario

Abstract

The interest in activity sensitivity from both the academics and the practitioners lies in the need to focus a project manager's attention on those activities that influence the performance of the project. When management has a certain feeling of the relative sensitivity of the various parts (activities) on the project objective, a better management's focus and a more accurate response during project tracking should positively contribute to the overall performance of the project. In the current research manuscript, a simulation study is performed to measure the ability of four basic sensitivity metrics to dynamically improve the time performance during project execution. We measure the use of sensitivity information to guide the corrective action decision making process to improve a project's time performance, while varying the degree of management's attention. A large amount of simulation runs are performed on a large set of fictitious project networks generated under a controlled design.

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  • Vanhoucke, Mario, 2010. "Using activity sensitivity and network topology information to monitor project time performance," Omega, Elsevier, vol. 38(5), pages 359-370, October.
  • Handle: RePEc:eee:jomega:v:38:y:2010:i:5:p:359-370
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    Cited by:

    1. Wauters, Mathieu & Vanhoucke, Mario, 2017. "A Nearest Neighbour extension to project duration forecasting with Artificial Intelligence," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1097-1111.
    2. Cui, Nanfang & Demeulemeester, Erik & Bie, Li, 2016. "Incorporation of activity sensitivity measures into buffer management to manage project schedule riskAuthor-Name: Hu, Xuejun," European Journal of Operational Research, Elsevier, vol. 249(2), pages 717-727.
    3. Mick Van Den Eeckhout & Broos Maenhout & Mario Vanhoucke, 2020. "Mode generation rules to define activity flexibility for the integrated project staffing problem with discrete time/resource trade-offs," Annals of Operations Research, Springer, vol. 292(1), pages 133-160, September.
    4. Fernando Acebes & Javier Pajares & José M. González-Varona & Adolfo López-Paredes, 2021. "Project risk management from the bottom-up: Activity Risk Index," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(4), pages 1375-1396, December.
    5. Fernando Acebes & Javier Pajares & Jose M Gonzalez-Varona & Adolfo Lopez-Paredes, 2024. "Project Risk Management from the bottom-up: Activity Risk Index," Papers 2406.00078, arXiv.org.
    6. Zuo, Fei & Zio, Enrico & Xu, Yue, 2023. "Bi-objective optimization of the scheduling of risk-related resources for risk response," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    7. Colin, Jeroen & Vanhoucke, Mario, 2014. "Setting tolerance limits for statistical project control using earned value management," Omega, Elsevier, vol. 49(C), pages 107-122.
    8. Martens, Annelies & Vanhoucke, Mario, 2017. "A buffer control method for top-down project control," European Journal of Operational Research, Elsevier, vol. 262(1), pages 274-286.
    9. Tao, Liangyan & Wu, Desheng & Liu, Sifeng & Lambert, James H., 2017. "Schedule risk analysis for new-product development: The GERT method extended by a characteristic function," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 464-473.
    10. Vanhoucke, Mario, 2011. "On the dynamic use of project performance and schedule risk information during projecttracking," Omega, Elsevier, vol. 39(4), pages 416-426, August.
    11. Ünsal-Altuncan, Izel & Vanhoucke, Mario, 2024. "A hybrid forecasting model to predict the duration and cost performance of projects with Bayesian Networks," European Journal of Operational Research, Elsevier, vol. 315(2), pages 511-527.
    12. Martens, Annelies & Vanhoucke, Mario, 2019. "The impact of applying effort to reduce activity variability on the project time and cost performance," European Journal of Operational Research, Elsevier, vol. 277(2), pages 442-453.
    13. Stefan Creemers & Erik Demeulemeester & Stijn Vonder, 2014. "A new approach for quantitative risk analysis," Annals of Operations Research, Springer, vol. 213(1), pages 27-65, February.
    14. She, Bingling & Chen, Bo & Hall, Nicholas G., 2021. "Buffer sizing in critical chain project management by network decomposition," Omega, Elsevier, vol. 102(C).
    15. Zhenhua Hu & Shanshan Jin & Ziyue Hu & Degen Lin, 2022. "Research on Attention Allocation of Land Policy System Reform: A Comparative Analysis Based on Central No. 1 Documents of China," Sustainability, MDPI, vol. 14(23), pages 1-16, November.
    16. Vaseghi, Forough & Martens, Annelies & Vanhoucke, Mario, 2024. "Analysis of the impact of corrective actions for stochastic project networks," European Journal of Operational Research, Elsevier, vol. 316(2), pages 503-518.
    17. Song, Jie & Martens, Annelies & Vanhoucke, Mario, 2021. "Using Schedule Risk Analysis with resource constraints for project control," European Journal of Operational Research, Elsevier, vol. 288(3), pages 736-752.
    18. Song, Jie & Martens, Annelies & Vanhoucke, Mario, 2020. "The impact of a limited budget on the corrective action taking process," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1070-1086.
    19. Plaza, Malgorzata, 2016. "Balancing the costs of human resources on an ERP project," Omega, Elsevier, vol. 59(PB), pages 171-183.
    20. Zarghami, Seyed Ashkan & Dumrak, Jantanee, 2021. "Aleatory uncertainty quantification of project resources and its application to project scheduling," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    21. Junguang Zhang & Dan Wan, 2021. "Determination of early warning time window for bottleneck resource buffer," Annals of Operations Research, Springer, vol. 300(1), pages 289-305, May.

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